Meta learning is also named learning to learn [1]}, [2]}, [3]} because its goal is to help the machine to be capable of learning quickly, especially in the case with very few samples for the new task(s). Generally speaking, meta learning algorithms are composed of two components, i.e. base learner and meta learner. According to their roles, base learner is mainly in charge of handling with individual tasks, and meta learner is much like a coordinator, through learning individuals tasks, meta learner can boost the performance of base learners across the tasks.
